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Found 8 papers, 4 papers with code
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
A Deep Reinforcement Learning Chatbot
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem.
Piecewise Latent Variables for Neural Variational Text Processing
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders.
On the Evaluation of Dialogue Systems with Next Utterance Classification
An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data.
How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available.
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models.
